CN109783995A - A kind of improved system risk Matrix Analysis Method - Google Patents
A kind of improved system risk Matrix Analysis Method Download PDFInfo
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- CN109783995A CN109783995A CN201910175027.7A CN201910175027A CN109783995A CN 109783995 A CN109783995 A CN 109783995A CN 201910175027 A CN201910175027 A CN 201910175027A CN 109783995 A CN109783995 A CN 109783995A
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Abstract
The invention discloses a kind of improved system risk Matrix Analysis Method, this method is suitable for the assessment of space experiment engineering system risk quantification and decision.A kind of improved system risk Matrix Analysis Method proposed, firstly, risk potential information risk probability and consequence attention rate are characterized, secondly, the risk index computation model based on attention rate coefficient is devised, again, provides the risk Metrics grade distribution method based on attention rate coefficient.The system risk Matrix Analysis Method of a modification of the present invention is realized is included in risk class quantization and decision process for the cognition preference of decision in the face of risk person in a manner of quantification, the science and confidence level of system risk grade quantizing result are effectively improved, risk analysis cost is reduced.
Description
Technical field
The invention belongs to space experiment engineering fields, are a kind of improved system risk grade quantizing analysis methods.
Background technique
Risk Metrics realize key risk situation etc. according to risk possibility occurrence and sequence severity two dimensional attributes feature
Grade quantization.Due to the complexity and process dynamics of space experiment task, traditional risk Metrics analytic approach highlights deficiency, first is that depositing
Assessment result inaccurateization the problem of [in Zhu Qichao, Kuang Xinghua, Shen Yongping risk matrix and application commentary [J]
State engineering science .2003]；Second is that risk class distributes reasonability by query [Aven T, Heide B.Reliability
and validity of risk analysis[J].Reliability Engineering&System Safety.2009]。
For the inaccurate problem of risk matrix method assessment result, [Chen Jian, Li Zhongmin, Tang Shuchun wait to be based on to document
Improve Weapon System Acquisition risk assessment [J] system engineering and the electronic technology .2008 of risk matrix] by increasing wind
The mode of dangerous weight and the measure of risk class is refined to improve the fine granularity of risk evaluation result；[Japanese plum is clear, face for document
Intelligence, application [J] China Safety Science journal .2010 of the Duan Yu risk matrix method in Dangerous and Harmful Factors classification] it utilizes
Bardo sequence value method carries out risk source sequence.Both the above method substantially cannot be completely eliminated risk knot.
For the unstability for describing risk Metrics assessment result, document [Markowski A S, Mannan M S.Fuzzy
Risk matrix. [J] .Journal of Hazardous Materials.2008] propose fuzzy risk matrix analysis side
Method, the value-at-risk of risk profiles is solved by specific function form, but can not really reflect decision in the face of risk person to risk profiles
Recognize preference.
Match reasonability problem for risk Metrics ranking score, there are two class methods: first is that the distribution based on risk class ordinal number
Method, a kind of comprehensive judging method combined by possibility grade ordinal number with consequence grade ordinal number meet risk congruence increasing
Long principle；It is the risk class distribution method based on expertise knowledge second is that being based on " IF-THEN " heuristic method.Currently,
To the quantization signifying of risk class ordinal number method risk two dimensional attributes, document [Ni H H, An C, Ning C.Some
Extensions on risk matrix approach. [J] .Safety Science.2010] propose possibility and consequence etc.
Grade range should use linear expression mode；Document [Flage R, W.A Reflection on Some Practices
In the Use of Risk Matrices [C] .2012] it studies and thinks, possibility and consequence rate range use exponential representation
Better than linear expression, and provide the risk index calculation method of grade ordinal number addition rule Yu multiplication principle；Document [Jr C
L.What's wrong with risk matrices? [J] .Risk Analysis.2008] according to risk Metrics individual hierarchical
Risk class is divided into basic, normal, high three-level, and to risk Metrics member by apart from risk Metrics origin distance by symmetrical characteristic
Plain ranking score is with proposition 3 weak consistency, intermediateness and ranking consistence axioms.
The above research does not consider that decision in the face of risk person recognizes the uncertain influence to the distribution of risk Metrics individual hierarchical,
Including potential informations such as risk controllability, risk manageability and the urgent degree of risk.It is main insufficient are as follows: traditional risk square
Battle array analysis method distributes risk elements grade only in accordance with grade symmetry principle, and one is a lack of consideration risk index in risk Metrics
In distribution trend characteristic, two are a lack of and consider the influence that distributes risk class of risk potential information.
Summary of the invention
The object of the present invention is to provide one kind for space experiment engineering system risk class under potential risk informational influence
Quantitative analysis method.With this method, distribution of the risk index in risk Metrics is calculated, improves risk Metrics member
The science and reasonability that plain ranking score is matched.
Present invention has an advantage that
(1) the system risk Matrix Analysis Method of a modification of the present invention realizes risk potential information in risk Metrics
Middle characterization recognizes preference by decision in the face of risk person to portray risk potential information；
(2) the system risk Matrix Analysis Method of a modification of the present invention proposes the concept of risk degree of concern coefficient,
Realize that quantificational description decision in the face of risk person recognizes the influence that preference matches risk Metrics ranking score.
Detailed description of the invention
The system risk Matrix Analysis Method flow chart of Fig. 1 a modification of the present invention；
β < α risk index distribution schematic diagram Fig. 2 of the invention；
α=β risk index distribution schematic diagram Fig. 3 of the invention；
α < β risk index distribution schematic diagram Fig. 4 of the invention；
Fig. 5 virtual risk index schematic diagram of calculation result of the invention based on CPCD；
Fig. 6 risk Metrics element sequence schematic diagram of the invention based on concern coefficient.
Specific embodiment
It is described in further detail in conjunction with system risk Matrix Analysis Method of the attached drawing to a modification of the present invention.
Step 1: being recognized by key risk source, identifies system critical risk factor that may be present；
Step 2: risk class is set as 5 grades, risk possibility is 7 grades, and risk schedule severity is 5 grades；
Step 3: calculation risk probability is synthesized if data are insufficient using domain-specialist knowledge；
Step 4: risk attention rate coefficient, calculation risk index are determined.
Traditional ranking score based on grade ordinal number method and subjective decision method is matched there are nonuniformity, is lacked risk index and is existed
Potential risk information influences in risk Metrics the considerations of distribution trend, and under different engineering-environments, and such as manageability can control
Property, criticality etc..Currently, about risk potential information not yet generally acknowledged quantitative criteria [Duan Y, Zhao J, Chen J,
et al.A risk matrix analysis method based on potential risk influence:A case
study on cryogenic liquid hydrogen filling system[J].Process Safety&
Environmental Protection.2016,102:277-287.], pass through decision in the face of risk person's experience in practice in engineering and knows
Know judgement description namely perception of risk preference, the present invention are defined as risk attention rate, is indicated with attention rate coefficient.
Shown in risk index model such as formula (1) after introducing risk attention rate coefficient:
R=Pα×Cβ, 0 < α, 0 < β, alpha+beta=1 (1)
In formula: R is risk index；P is possibility grade ordinal number variable；C is consequence grade ordinal number variable；α is possibility
Attention rate coefficient；β is consequence attention rate coefficient.
To study the risk index distribution characteristics under different attention rate coefficients, continuous probability consequence distribution map (CPCD) is utilized
The risk index regularity of distribution is described.Without loss of generality, 3 kinds of situations is divided to be discussed.
1.0 < β of situation < α, it is assumed that α=0.7, β=0.3, calculated CPCD and risk index distribution such as Fig. 2 (a) are shown,
Shown in risk index section such as Fig. 2 (b).
As can be seen that risk index constitutes irregular 5 risk class bands from Fig. 2 (a), risk index distribution is more leaned on
Nearly probability reference axis, i.e., when decision in the face of risk person focuses more on the probability attribute of risk, the risk index close to probability reference axis is big
In the risk index close to consequence reference axis.
2. α of situation=β=0.5, when decision in the face of risk person holds identical attention rate coefficient, α=β=0.5 calculates CPCD
It is as shown in Figure 3 with risk index distribution.
3.0 < α of situation < β, if decision in the face of risk person to consequence attribute degree of concern be greater than probability degree of concern, α=0.3, β=
0.7, it calculates CPCD and risk index distribution is as shown in Figure 4.
As seen from Figure 4, risk index is distributed relatively close risk schedule attribute coordinate axis, and as α → 0, at this time
It is single Consequential Loss that risk index, which is degenerated,.
Step 5: risk possibility grade and the ordinal number serialization of consequence grade are counted according to grade ordinal number " multiplication principle "
Risk index is calculated, it is risk class that greateset risk exponent pair, which answers tier definition, in matrix element.Wherein, risk two-dimensional coordinate grade
Range is all made of exponential representation form, and risk potential information is provided by risk expertise comprehensive descision, and with possibility with
Consequence pays close attention to coefficient quantization.
By taking 4 × 4 risk Metrics as an example, it is assumed that by risk expert's comprehensive descision obtain three groups of concern factor alphas=0.3, β=
0.7, α=β=0.5 and α=0.7, β=0.3, risk class N=4, possibility grade P=4, consequence grade C=4, according to upper
The process of stating calculate 4 × 4 risk Metrics are as shown in Figure 5.
According to CRM risk class distribution method, the risk Metrics element R of calculating14、R24、R23、R32、R33、R42With R41Grade
It is equal；Using the mentioned method of the present invention, in the case that concern coefficient is constant, corresponding risk Metrics individual hierarchical distribution is such as Fig. 6 institute
Show.
Compared to CRM analysis method, when risk concern coefficient changes, risk Metrics individual hierarchical changes, variation compared with
Big element is the low consequence region of high probability and the high consequence region of low probability.
Step 6: risk class compares, and judges whether risk class meets engineering system objective reality, otherwise returns to third
Step, recalculates risk probability.
Step 7: risk class obtained by calculation carries out decision in the face of risk and control.
Claims (1)
1. a kind of improved system risk Matrix Analysis Method, comprising:
Step 1: key risk source recognizes；
Step 2: defining possibility and consequence grade and its quantizing range；
Step 3: risk probability calculates；
Step 4: determining risk attention rate coefficient；
Step 5: risk index calculates；
Step 6: risk Metrics individual hierarchical distributes；
Step 7: decision in the face of risk and control.
It is characterized by:
Step 1: recognizing by key risk source, system critical risk factor that may be present is identified, be subsequent risk class
Quantization lays the foundation；
Step 2: specifying each grade pair of specific risk by defining risk Metrics possibility occurrence grade and risk schedule grade
The numberical range answered；
Step 3: calculation risk probability is synthesized if data are insufficient using domain-specialist knowledge；
Step 4: determining risk attention rate factor alpha and β using Delphi method；
Step 5: by R=Pα×CβRisk index is calculated, and the continuous probability consequence figure (CPCD) of calculating is mapped to biography
It unites risk Metrics (CRM)；
Step 6: determining risk Metrics individual hierarchical by greateset risk index in risk Metrics element；
Step 7: risk class obtained by calculation, carries out decision in the face of risk and control.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112381475A (en) * | 2021-01-15 | 2021-02-19 | 西南石油大学 | Gas distribution station anti-seismic safety evaluation method and evaluation system |
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|---|---|---|---|---|
| CN102866984A (en) * | 2012-05-03 | 2013-01-09 | 北京国政通科技有限公司 | Matrix quantitative analysis method in intelligent transaction and system thereof |
| CN104408572A (en) * | 2014-12-02 | 2015-03-11 | 中国石油大学(华东) | Semi-quantitative risk analysis method for gas pipeline industry based on risk matrix |
| CN106685921A (en) * | 2016-11-14 | 2017-05-17 | 中国人民解放军信息工程大学 | Network equipment risk assessment method |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102866984A (en) * | 2012-05-03 | 2013-01-09 | 北京国政通科技有限公司 | Matrix quantitative analysis method in intelligent transaction and system thereof |
| CN104408572A (en) * | 2014-12-02 | 2015-03-11 | 中国石油大学(华东) | Semi-quantitative risk analysis method for gas pipeline industry based on risk matrix |
| CN106685921A (en) * | 2016-11-14 | 2017-05-17 | 中国人民解放军信息工程大学 | Network equipment risk assessment method |
Non-Patent Citations (1)
| Title |
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| "一种考虑认知不确定性的风险矩阵分析方法" * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112381475A (en) * | 2021-01-15 | 2021-02-19 | 西南石油大学 | Gas distribution station anti-seismic safety evaluation method and evaluation system |
| CN112381475B (en) * | 2021-01-15 | 2021-03-26 | 西南石油大学 | Gas distribution station anti-seismic safety evaluation method and evaluation system |
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